The present invention relates generally to diagnostic imaging and, more particularly, to a method and apparatus of diagnostic imaging with material discrimination capabilities.
Exemplary diagnostics devices comprise x-ray systems, magnetic resonance (MR) systems, ultrasound systems, computed tomography (CT) systems, positron emission tomography (PET) systems, ultrasound, nuclear medicine, and other types of imaging systems. Typically, in CT imaging systems, an x-ray source emits a fan-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces a separate electrical signal indicative of the attenuated beam received by each detector element. The electrical signals are transmitted to a data processing system for analysis which ultimately produces an image.
Generally, the x-ray source and the detector array are rotated about the gantry opening within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam at a focal point. X-ray detectors typically include a collimator for collimating x-ray beams received at the detector, a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom.
Typically, each scintillator of a scintillator array converts x-rays to light energy. Each scintillator discharges light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are then transmitted to the data processing system for image reconstruction.
An exemplary CT imaging system comprises an energy discriminating (ED), multi energy (ME), and/or dual energy (DE) CT imaging system that may be referred to as an EDCT, MECT, and/or DE-CT imaging system. The EDCT, MECT, and/or DE-CT imaging system in an example is configured to be responsive to different x-ray spectra. For example, a conventional third generation CT system acquires projections sequentially at different x-ray tube potentials. Two scans in an example are acquired either back to back or interleaved in which the tube operates at 80 kVp and 160 kVp potentials. Special filters in an example are placed between the x-ray source and the detector such that different detector rows collect projections of different x-ray energy spectra. The filters are often placed between the x-ray source and the scanned object. The special filters that shape the x-ray spectrum in an example can be used for two scans that are acquired either back to back or interleaved. Energy sensitive detectors in an example are used such that each x-ray photon absorbed in the detector is recorded with its photon energy.
Exemplary ways to obtain the measurements comprise: (1) scan with two distinctive energy spectra, (2) detect photon energy according to energy deposition in the detector, and (3) photon counting with multiple energy bins. EDCT/MECT/DE-CT provides energy discrimination and material characterization. In the absence of object scatter, the system in an example derives the information about object attenuation versus energy based on the signal from two or more regions of photon energy in the spectrum, for example, the low-energy and the high-energy portions of the incident x-ray spectrum. In an exemplary energy region of medical CT, two physical processes dominate the x-ray attenuation: (1) Compton scatter and the (2) photoelectric effect. The detected signals from two energy regions provide sufficient information to resolve the energy dependence of the material being imaged. Furthermore, detected signals from the two energy regions provide sufficient information to determine the relative composition of an object composed of two materials.
The conventional material basis decomposition or basis material decomposition (BMD) algorithm is based on the concept that in the energy region for medical CT, the x-ray attenuation of any given material can be represented by a proper density mix of two other materials, referred to as the basis materials. The BMD algorithm acquires two CT images. Each of the CT images represents the equivalent density of one of the basis materials. Since a material density is independent of x-ray photon energy, these images are approximately free of beam-hardening artifacts. An operator can choose the basis material to target a certain material of interest, for example, to enhance the image contrast.
Energy discrimination (ED) basis material decomposition (BMD) images are noisier than the conventional CT image. Conventional CT images represent the x-ray attenuation of an object under investigation. Without any energy information from the detection system, the conventional CT image cannot provide material characterization information. Two different materials with different densities may have similar CT numbers. In order to overcome this fundamental limitation and decode the BMD information, an exemplary EDCT system needs to separately detect at least two regions of photon energy spectrum: the low-energy and the high-energy portions of the incident x-ray spectrum. In another example, the EDCT system can detect all photons at different energies but with at least two different energy outputs (e.g., bins) that weight the photons differently as a function of energy. As a result, the total x-ray flux is divided into at least two subgroups. The quantum noise from each subgroup is larger than the quantum noise for the entire spectrum. Therefore, the material decomposition images are noisier than the conventional CT image at the same conditions. A typical way of decreasing image noise is using a linear filter that replaces each image pixel value with a weighted sum of pixels in a neighborhood of that pixel. Such a linear filter can be implemented in image space or Fourier space. In either case, a characteristic feature of a linear filter is that the noise decrease is accompanied by a spatial resolution decrease. Other filtering methods such a median filter or adaptive filter are non-linear and can preserve sharp edges while reducing noise in regions of low gradient. Whereas the linear filter minimizes the mean-square difference, the median filter minimizes the absolute difference. A FIR-median filter is one that implements a median filter with increased and/or more optimum processing speed by reusing previous calculated results as moving from pixel to pixel. As will be understood by those skilled in the art, the median filter is implemented by replacing each pixel value by one of the values in the local neighborhood of pixels that minimizes the absolute difference summed over all the pixels in this neighborhood. Because such a calculation involves pairwise differences between pixel values, these pairwise differences can be precalculated and the sum made by combining these precomputed values for the neighborhood of the examined pixel. When filtering an image, one starts at some examined pixel location and replaces its value with the median value over the neighboring pixels. Then the next examined point in the data is processed by the filter. This requires a different sum, but this sum is closely related to the sum of the first examined pixel. The FIR-median filter makes use of this fact and only updates the sum as the filter method moves from pixel to pixel.
Therefore, it would be desirable to design an apparatus and method that employs response information from multiple bins and combines multi-bin data from neighboring pixels to reduce the noise of material decomposition images. It would be further desirable to decrease the noise with substantially little and/or minimal impact on the spatial resolution.